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概要と使いどころ
Display input/output args, return object and cost time of a Python method invocation in a live process. Use this to observe function behavior at runtime — see what arguments go in, what comes back, and how long it takes. This is the most frequently used diagnostic command.
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Hot-reload a Python function implementation from the latest file content without restarting the process. Use this to apply code fixes live after identifying a bug with watch/trace.
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Explainability (XAI) audit for products containing AI components. Five-stage tier-scaled check: risk classification, stakeholder×question matrix, fidelity audit, system card, recourse path. Honest about what was validated functionally vs what needs user testing.
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Map the Jobs to be Done (functional, emotional, and social) that people are trying to get done. Based on Christensen's JTBD theory — applies to any object, not only software.
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Apply MCAF non-functional-requirements guidance to capture or refine explicit quality attributes such as accessibility, reliability, scalability, maintainability, performance, and compliance. Use when a feature or architecture change needs explicit quality attributes and trade-offs.
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Use for AST search and codemods. Triggers: ast-grep, sg, function/call/class/import, empty catch, missing await, YAML rules.
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Reference for string algorithm patterns — KMP, Z-function, Rabin-Karp, Manacher's, string hashing, suffix arrays, and Aho-Corasick. Covers recognition signals, core ideas, Python templates, edge cases, and common mistakes for each technique.
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Verify that implementation code faithfully implements its specification — checks function contracts, API contracts, and protocol compliance against source specifications
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Create distinctive, production-grade web interfaces with intentional aesthetic direction. Avoids generic AI aesthetics (centered layouts, Inter/Arial, purple gradients, uniform corners). Use for UI components, dashboards, landing pages, React/HTML/CSS layouts where design quality matters.
日本語の概要は準備中です。原文の説明を表示しています。
Requires detailed type annotations for all Python functions, methods, and class members.
日本語の概要は準備中です。原文の説明を表示しています。
Applies general rules for Convex development, emphasizing schema design, validator usage, index-first query patterns, function registration, and correct handling of system fields.
日本語の概要は準備中です。原文の説明を表示しています。
Architecture review and design validation. Evaluates system designs against best practices, identifies anti-patterns, and ensures architectural decisions align with non-functional requirements.
日本語の概要は準備中です。原文の説明を表示しています。
Ultra-granular code analysis for deep architectural context building. Line-by-line and block-by-block analysis using First Principles, 5 Whys, 5 Hows methodology at micro scale. Builds mental models, tracks invariants and assumptions, and maps cross-function call flows for security audit preparation.
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Build a mental and visual map of the target application before hunting — entry points, trust boundaries, data flows, and high-value functionality. Use when user has scope parsed and wants to plan WHERE to hunt rather than diving into recon.
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Generate 5 distinct, production-grade UI variations for a requested new or existing section, component, page, flow, or shell, then help the user preview and apply one. Use when the user asks to add or redesign UI like a hero, pricing, navbar, auth flow, dashboard shell, ecommerce surface, or data component.
日本語の概要は準備中です。原文の説明を表示しています。
Create distinctive, production-grade frontend interfaces with strong hierarchy, thoughtful systems, and polished implementation that avoid generic AI aesthetics. Use when the user wants to build or redesign web pages, flows, components, or full app surfaces, or when another better-web-ui skill needs shared project design context before other better-web-ui skills.
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Perform a final quality pass fixing alignment, spacing, consistency, and micro-detail issues before shipping. Use when the work is functionally complete and needs finishing touches—not when the hierarchy, structure, tone, or technical foundation still need major changes.
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Guide for adding new wsh commands to Wave Terminal. Use when implementing new CLI commands, adding command-line functionality, or extending the wsh command interface.
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Guide for adding new RPC calls to Wave Terminal. Use when implementing new RPC commands, adding server-client communication methods, or extending the RPC interface with new functionality.
日本語の概要は準備中です。原文の説明を表示しています。
Build AI agents using the Claude Agent SDK. Covers query functions, ClaudeSDKClient, custom tools, MCP servers, hooks, permissions, subagents, and browser control (Chrome extension, dev-browser skill). Use when creating agents, adding tools, configuring agent behavior, or automating browser tasks.
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Build distinctive, production-grade frontend interfaces — websites, landing pages, dashboards, React components, HTML/CSS layouts, editorial heroes with depth-parallax (Astryx pattern), pointer-reactive fluid effects, pixel-glitch canvas heroes and terminal-style tool-panel dioramas (Composio pattern). Avoids generic AI aesthetics. Triggers on web UI, styling, beautify, frontend design, dashboard, landing page, React component, editorial hero, glitch hero, dev-tool landing page.
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Work on bifunctional degraders and molecular glues, where potency comes from a ternary complex rather than occupancy. Use this skill to apply the property rules that govern this beyond-rule-of-five space, reason about linker length, attachment vector and E3 ligase choice, prepare inputs for ternary complex structure prediction, and interpret degradation readouts — DC50, Dmax, cooperativity, and the hook effect that makes a dose-response curve turn over. Also trigger on PROTAC, molecular glue, targeted protein degradation, E3 ligase, cereblon, VHL, ternary complex, DC50, Dmax, hook effect, cooperativity, or PROTAC-DB.
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Number antibody variable domains, annotate CDRs, and assess developability from sequence. Use this skill to apply IMGT, Kabat, Chothia, Martin, or AHo numbering with ANARCI, delimit CDRs and framework regions, scan for chemical liabilities (N-glycosylation sequons, deamidation NG, isomerisation DG, oxidation, unpaired cysteine, fragmentation), compute pI, net charge, extinction coefficient and hydrophobicity, and plan humanisation by CDR grafting. Also trigger on antibody, nanobody, VHH, scFv, Fab, CDR, framework, ANARCI, abnumber, IgBLAST, OAS, SAbDab, humanization, Vernier residues, or developability.
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Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.
日本語の概要は準備中です。原文の説明を表示しています。